1. Supervised learning

This is broad category with a few subtypes. Essentially it means we train the algorithm on some correct examples. So in our cat example, we would show the algorithm some cat pictures until it gets the idea and can start recognizing cats in other pictures.

2. Semi-supervised learning

This is a subtype where the algorithm is trained on both labeled and unlabeled data. In other words there are a bunch of pictures labeled as cats but there are also a bunch of unlabeled pictures which may or may not have cats. It uses the labeled one to help figure out the unlabeled ones until again it can start recognizing cats in any picture.

3. Reinforcement learning

This one only gives training data in response to actions. It's really good for things like driving or games. Proper actions are reinforced, improper ones lead to failure like losing the game.

4. Active learning

This is one where the algorithm can get labels for a limited amount of data, so it's best to make guesses when it thinks it will be right.

5. Unsupervised learning

This is where no labeled data is involved at all and no feedback is delivered, so no reinforced learning. An example might be what's called General Adversarial Networks, where two neural networks compete and success in competition drives the learning. Its especially promising in security for detecting advanced or previously unknown types of attacks.

Well now you have learned how the machines learn and maybe this will help keep you smarter than the machines!

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About Tom Merritt

Tom is an award-winning independent tech podcaster and host of regular tech news and information shows. Tom hosts Sword and Laser, a science fiction and fantasy podcast, and book club with Veronica Belmont. He also hosts Daily Tech News Show, coverin...

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Tom is an award-winning independent tech podcaster and host of regular tech news and information shows. Tom hosts Sword and Laser, a science fiction and fantasy podcast, and book club with Veronica Belmont. He also hosts Daily Tech News Show, covering the most important tech issues of the day with the smartest minds in technology.